7. Visualizing & exporting

• Load into the GUI — the keypoints become the session’s features, and a copy is saved beside your video as <video>.keypoints.nc.

  Nothing is grafted onto the dataset you had open: the keypoints are a dataset, so they replace what serves features, and keypoint and individual arrive as ordinary dimensions you pick in the right sidebar next to space (x/y). Your video, audio, panels and layout are untouched — only the data under them changes.

  A feature plot draws one line per value of one dimension, so picking a keypoint feature pins the rest automatically; the Space / Keypoint / Individual dropdowns change which one is free. If a panel looks empty, that is the first thing to check.

• Head direction (from marker orientation) — TODO replace with visual example.

• Export poses (NetCDF)… — a movement-compatible poses dataset covering every frame of the video; frames outside the filled span carry NaN position and NaN confidence, which is how movement represents a missing point (see Everything happens between your labels). Head direction, if ticked, is written alongside position as head_direction and heading.

• Coordinate space — pixels (source-video image coordinates, the default) or cm (calibrated), offered once the Calibrate tab holds three ready landmarks. A cm export maps positions — and everything derived from them — through the landmark fit, records the unit as attrs["space_unit"] and the matrix as attrs["pixels_to_cm"]. The y-flip then mirrors the world frame’s y axis (y → −y) after the transform, rather than the image’s pixels — untick it if your landmark coordinates already read the way you want. The space dimension stays x/y either way.

Note

Velocity, speed and acceleration are measured between the frames a point was actually seen on — labelled or filled. With a handful of labels and no fill that means one value per labelled frame (the average velocity across each gap) and NaN in between, rather than nothing at all. Run Fill first if you want them frame by frame.